A negative selection algorithm with human-in-the-loop for anomaly detection

Article type: Research Article

Authors: Li, Chunling^{a; b; *} | Zhang, Yi^{b; c}

Affiliations: [a] The School of Business, Changzhou University, Jiangsu Province, China | [b] Jiangsu Provincial Institute of Technology Transfer (Changzhou University), Jiangsu Province, China | [c] School of Mechanical Engineering and Rail Transit, Changzhou University, Jiangsu Province, China

Correspondence: [*] Corresponding author. Chunling Li, E-mail: [email protected].

Abstract: The existing negative selection algorithms can not improve their detection performance by human intervention during the testing process. This paper proposes a negative selection algorithm with human-in-the-loop for anomaly detection. It uses self-sample clusters to train detectors with a nonrandom strategy. Its detectors and self-sample clusters fully cover state space without overlapping each other. It locally adjusts detectors and self-sample clusters with human intervention to improve its detection performance during the testing process. Experiments were performed on two synthetic datasets and the Iris dataset from the UCI repository to assess its performance. The results show that it outperforms the other anomaly detection methods in most cases.

Keywords: Negative selection algorithm, human-in-the-loop, anomaly detection, artificial immune algorithm, artificial immune system

DOI: 10.3233/JIFS-235724

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9367-9380, 2024